Deep High-Level Policy Model Predictive Contour Control for Autonomous Racing

For autonomous racing vehicles, it is very important to perform fast overtaking maneuvers in a safe manner. To this aim, we design a high-level policy for model predictive contouring control (High-MPCC). Two decision variables will be obtained from the high-level policy using the observations of the ego and lead vehicle. Based on these two decision variables, the ego vehicle will determine whether to perform an overtaking maneuver when there is an opponent vehicle. These two decision variables will also be used as the control parameters of the lower model predictive contouring control (MPCC) controller to adjust the cost function of the MPCC controller. Then an overtaking maneuver or maintaining a safe distance from the lead vehicle is performed. To improve the real-time performance of the algorithm, the combination of deep neural network and policy search is introduced to form deep High-MPCC. Simulation results show that the proposed High-MPCC controller performs better than traditional MPCC, especially in the process of overtaking.